manufacturing 1.1.8

Six-Sigma based analysis of manufacturing data

Purpose

To provide analysis tools and metrics useful in manufacturing environments.

I am slowly generating the documentation and, as that is maturing, I will begin to move information from this readme.md into that location. If you don't find something here, head over to the documentation.

Project Maturity

Every effort is being made to ensure that the results are accurate, but the user is ultimately responsible for any resulting analysis.

The API should not be considered stable until v1.0 or greater. Until then, breaking changes may be released as different API options are explored.

During the v0.X.X versioning, I am using the package in my own analyses in order to find any bugs. Once I am reasonably satisfied that the package is feature complete, usable, and bug-free, I will break out the v1.X.X releases.

Installation

To install from pypi:

pip install manufacturing

To install from source download and install using poetry:

poetry install

Usage

Visualizations with Jupyter Notebooks

Visualizations work approximately as expected within a jupyter notebook.

data = np.random.normal(0, 1, size=30)  # generate some data
manufacturing.ppk_plot(data, lower_specification_limit=-2, upper_specification_limit=2)

There is a sample jupyter notebook in the examples directory.

Cpk Visualization

The most useful feature of the manufacturing package is the visualization of Cpk. As hinted previously, the ppk_plot() function is the primary method for display of Cpk visual information. First, get your data into a list, numpy.array, or pandas.Series; then supply that data, along with the lower_control_limit and upper_control_limit into the ppk_plot() function.

manufacturing.ppk_plot(data, lower_specification_limit=-2, upper_specification_limit=2)

In this example, it appears that the manufacturing processes are not up to the task of making consistent product within the specified limits.

Zone Control Visualization

Another useful feature is the zone control visualization.

manufacturing.control_chart(data)

There are X-MR charts, Xbar-R charts, and Xbar-S charts available as well. If you call the control_chart() function, the appropriate sample size will be selected and data grouped as the dataset requires. However, if you wish to call a specific type of control chart, use

• x_mr_chart
• xbar_r_chart
• xbar_s_chart

RoadMap

Items marked out were added most recently.

• ...
• Add use github actions for deployment
• Transition to poetry for releases
• Add I-MR Chart (see examples/imr_chart.py)
• Add Xbar-R Chart (subgroups between 2 and 10)
• Add Xbar-S Chart (subgroups of 11 or more)
• Back with testing

Gallery